Abstract
This paper investigates the problem of updating over time the statistical language model (LM) of an Italian broadcast news transcription system. Statistical adaptation methods are proposed which try to cope with the complex dynamics of news by exploiting newswire texts daily available on the Internet. In particular, contemporary news reports are used to extend the lexicon of the LM, to minimize the out-of-vocabulary (OOV) word rate, and to adapt the n-gram probabilities. Experiments performed on 19 news shows, spanning a period of one month, showed relative reductions of 58% in OOV word rate, 16% in perplexity, and 4% in word error rate (WER).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
